# The advantages of dense marker sets for linkage analysis with very large families

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## Abstract

Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods.

## Keywords

Markov Chain Monte Carlo Variance Component Analysis International HapMap Project Large Pedigree Markov Chain Monte Carlo Technique## Notes

### Acknowledgments

The authors would like to thank Terry Speed for his suggestions during the genesis of this project. RT, SQ, JD and JS are supported by an NHMRC Capacity-Building grant, and JS is also supported by an NHMRC Transitional Institute Grant. JM is an NHMRC CJ Martin Fellow.

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